list_negative_values

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves: it doesn't specify what constitutes a 'feature' (e.g., columns, rows), how negative values are detected (e.g., numeric types only), what the output format is (e.g., a count number, a list), or any error handling. For a tool with no annotations, this leaves significant gaps in understanding its operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality ('How many features have negative values'), making it efficient and easy to parse. There's no wasted information, and it appropriately sized for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (analytical operation on tables), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'features' are, how the count is returned, or any behavioral details like error cases. For a tool that performs data analysis, more context is needed to use it effectively, especially without structured support from annotations or output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the parameter 'table_name' documented as 'Table name to list'. The description adds minimal value beyond this, as it mentions 'in a table' which aligns with the schema but doesn't provide additional context like table format requirements or examples. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to count how many features have negative values in a table. It specifies the verb 'list' (though 'count' would be more precise) and the resource 'features with negative values in a table'. However, it doesn't explicitly differentiate from siblings like list_missing_values or list_distinct_values, which perform similar analytical functions on tables.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like list_missing_values for missing data analysis or query for more complex filtering. There's no context about prerequisites, such as requiring the table to exist or have numeric features, nor any exclusions or recommendations for specific use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.